# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import numpy as np

digit8 = [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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digit7a = [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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          ]

digit7b = [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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          ]

digit5a = [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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digit5b = [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
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           [0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,4,4,0,0,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,0,4,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
           [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
           ]

def scale(x): return (np.array(x)/4.0).tolist()

sdigit8  = scale(digit8)
sdigit7a = scale(digit7a)
sdigit7b = scale(digit7b)
sdigit5a = scale(digit5a)
sdigit5b = scale(digit5b)
test_digits = [sdigit8, sdigit7a, sdigit7b, sdigit5a, sdigit5b]

if __name__ == '__main__':

    # for online predictions, ml-engine expects a valid JSON object with a list
    # print(json.dumps({'image': test_digits}))

    # "gcloud ml-engine [local] predict" expects one valid JSON object per line
    print(json.dumps({"image": sdigit8}))
    print(json.dumps({"image": sdigit7a}))
    print(json.dumps({"image": sdigit7b}))
    print(json.dumps({"image": sdigit5a}))
    print(json.dumps({"image": sdigit5b}))

    # This works too. In fact multiple images will be merged into a single tensor
    # whatever you do:
    # print(json.dumps({'image': test_digits}))

    # WARNING
    # If you sending JSON to the REST API directly (not through "gcloud ml-engine [local] predict")
    # The JSON must wrap all the above in a single "instances" JSON object like this:
    # {"instances": [{'image': sdigit8}, {'image': sdigit7a}]}

